911 lines
38 KiB
Python
911 lines
38 KiB
Python
import concurrent.futures
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import time
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import logging
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import threading
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from uuid import uuid4
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from typing import Dict, List, Optional
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import requests
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import os
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import json
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# -------------------------- 全局配置与常量定义 --------------------------
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# 日志配置
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[logging.StreamHandler()]
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)
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logger = logging.getLogger(__name__)
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# 任务状态定义
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TASK_STATUS = {
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"SUBMITTED": "待提交", # 初始状态
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"SUBMITTING": "提交中", # 提交过程中
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"SUCCEED": "提交成功", # 云际确认成功
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"FAILED": "提交失败", # 云际确认失败
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"RETRY_EXHAUSTED": "重试耗尽" # 超过最大失败次数
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}
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# 全局任务字典（key=target_id，value=任务详情）
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task_map: Dict[str, Dict] = {}
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task_map_lock = threading.Lock() # 任务字典线程锁
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# 全局数据集映射（key=file_location，value=DatasetInfo实例）- 新增
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dataset_map: Dict[str, Dict] = {}
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dataset_lock = threading.Lock() # 数据集映射线程锁
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# API配置
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API_CONFIG = {
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"login": {
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"url": "http://119.45.255.234:30180/jcc-admin/admin/login",
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"timeout": 10
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},
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"create_package": {
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"url": "http://119.45.255.234:30180/jsm/jobSet/createPackage",
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"timeout": 15
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},
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"upload_file": {
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"url": "http://119.45.255.234:30180/jcs/object/upload",
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"timeout": 3000
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},
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"notify_upload": {
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"url": "http://119.45.255.234:30180/jsm/jobSet/notifyUploaded",
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"timeout": 15
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},
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"bind_cluster": {
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"url": "http://119.45.255.234:30180/jsm/jobSet/binding",
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"timeout": 15
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},
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"query_binding": {
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"url": "http://119.45.255.234:30180/jsm/jobSet/queryBinding",
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"timeout": 15
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},
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"submit_task": {
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"url": "http://119.45.255.234:30180/jsm/jobSet/submit",
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"timeout": 100
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},
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"task_details": {
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"url": "http://119.45.255.234:30180/pcm/v1/core/task/details",
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"timeout": 15
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}
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}
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# 集群资源配置（key=集群ID，value=总资源/可用资源）
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cluster_resources: Dict[str, Dict] = {
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"1790300942428540928": { # modelarts集群
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"total": {"CPU": 512, "MEMORY": 1024, "NPU": 8},
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"available": {"CPU": 96, "MEMORY": 1024, "NPU": 8}
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}
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}
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cluster_lock = threading.Lock() # 集群资源线程锁
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# -------------------------- 数据结构定义 --------------------------
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class DatasetInfo(dict):
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"""数据集信息结构"""
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def __init__(self, file_location: str, name: str, size: float, **kwargs):
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super().__init__()
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self["file_location"] = file_location # 本地路径（主键）
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self["id"] = kwargs.get("id", str(uuid4())) # 数据集唯一标识
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self["name"] = name # 数据集名称
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self["size"] = size # 大小（字节）
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self["is_uploaded"] = kwargs.get("is_uploaded", False) # 是否已上传
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self["upload_cluster"] = kwargs.get("upload_cluster", []) # 上传的集群
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self["upload_time"] = kwargs.get("upload_time") # 上传时间
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self["description"] = kwargs.get("description") # 描述
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self["dataset_target_id"] = kwargs.get("dataset_target_id") # 绑定ID（新增）
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class AlgorithmInfo(dict):
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"""算法信息结构"""
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def __init__(self, cluster: str, id: str, name: str, **kwargs):
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super().__init__()
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self["cluster"] = cluster # 所属集群
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self["id"] = id # 算法唯一标识
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self["son_id"] = kwargs.get("son_id", "") # 子算法ID
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self["name"] = name # 算法名称
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class TaskInfo(dict):
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"""任务信息结构"""
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def __init__(self, task_name: str, dataset_name: str, son_code_id: int, resource: Dict, **kwargs):
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super().__init__()
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self["target_id"] = kwargs.get("target_id", str(uuid4())) # 任务唯一ID
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self["task_name"] = task_name # 任务名称
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self["package_name"] = kwargs.get("package_name", f"{task_name.lower()}-pkg") # 文件夹名称
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self["dataset_name"] = dataset_name # 关联数据集名称
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self["son_code_id"] = son_code_id # 子算法ID（直接从模板获取）
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self["resource"] = resource # 资源需求（CPU/MEMORY/NPU等）
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self["status"] = TASK_STATUS["SUBMITTED"] # 初始状态：待提交
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self["submit_time"] = kwargs.get("submit_time", time.strftime("%Y-%m-%d %H:%M:%S")) # 提交时间
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self["success_time"] = None # 成功时间（成功时填充）
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self["third_party_task_id"] = "" # 云际任务ID（提交后填充）
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self["file_location"] = kwargs.get("file_location", "") # 本地文件路径
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self["error_msg"] = "" # 错误信息
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self["fail_count"] = 0 # 失败次数
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self["max_fail_threshold"] = kwargs.get("max_fail_threshold", 3) # 最大失败阈值
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self["cluster_id"] = "" # 提交的集群ID（提交时填充）
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# -------------------------- 工具方法 --------------------------
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def generate_task_templates() -> List[Dict]:
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"""生成任务静态数据模板（包含son_code_id）"""
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return [
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{
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"task_name_template": "{prefix}-jointCloudAi-trainingtask",
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"prefix": "AA",
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"dataset_name": "data1.zip",
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"son_code_id": 1217, # 子算法ID直接定义在模板中
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"resource": {"CPU": 12, "MEMORY": 24, "NPU": 1},
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"file_location": "D:/数据集/cnn数据集/data1/"
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},
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{
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"task_name_template": "{prefix}-jointCloudAi-trainingtask",
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"prefix": "AB",
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"dataset_name": "cifar-10-python.tar.gz",
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"son_code_id": 1167,
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"resource": {"CPU": 12, "MEMORY": 24, "NPU": 1},
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"file_location": "D:/数据集/cnn数据集/data2/"
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},
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{
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"task_name_template": "{prefix}-jointCloudAi-trainingtask",
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"prefix": "AC",
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"dataset_name": "cifar-100-python.tar.gz",
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"son_code_id": 1169,
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"resource": {"CPU": 12, "MEMORY": 24, "NPU": 1},
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"file_location": "D:/数据集/cnn数据集/data3/"
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}
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]
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def load_tasks_to_queue(templates: List[Dict]) -> None:
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"""将任务静态数据加载到任务队列（使用son_code_id）"""
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global task_map
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with task_map_lock:
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task_map.clear()
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for idx, template in enumerate(templates):
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try:
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# 检查必填字段（更新为son_code_id）
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required_fields = ["task_name_template", "prefix", "dataset_name", "son_code_id", "resource",
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"file_location"]
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missing_fields = [f for f in required_fields if f not in template]
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if missing_fields:
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logger.warning(f"跳过无效任务模板（索引{idx}）：缺少字段 {missing_fields}")
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continue
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task_name = template["task_name_template"].format(prefix=template["prefix"])
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task = TaskInfo(
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task_name=task_name,
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dataset_name=template["dataset_name"],
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son_code_id=template["son_code_id"], # 直接使用模板中的子算法ID
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resource=template["resource"],
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file_location=template["file_location"]
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)
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task_map[task["target_id"]] = task
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logger.info(f"任务入队 | task_name: {task_name} | target_id: {task['target_id']}")
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except Exception as e:
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logger.error(f"加载任务模板失败（索引{idx}）：{str(e)}")
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logger.info(f"任务队列加载完成 | 共 {len(task_map)} 个有效任务")
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def select_cluster(task_resource: Dict) -> Optional[str]:
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"""根据任务资源需求选择合适的集群"""
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with cluster_lock:
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for cluster_id, cluster in cluster_resources.items():
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# 检查集群可用资源是否满足任务需求（支持NPU/DCU等不同加速卡类型）
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resource_match = True
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for res_type, required in task_resource.items():
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available = cluster["available"].get(res_type, 0)
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if available < required:
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resource_match = False
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break
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if resource_match:
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# 选中集群后更新可用资源（减去任务所需资源）
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for res_type, required in task_resource.items():
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if res_type in cluster["available"]:
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cluster["available"][res_type] -= required
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logger.info(f"选中集群 {cluster_id}，更新后可用资源: {cluster['available']}")
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return cluster_id
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logger.warning(f"无满足资源需求的集群 | 任务需求: {task_resource}")
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return None
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# -------------------------- 新增：数据集上传判断与处理方法 --------------------------
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def check_and_handle_dataset(file_location: str, dataset_name: str, cluster_id: str) -> Optional[str]:
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"""
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检查数据集是否已上传到指定集群，未上传则执行上传
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参数:
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file_location: 本地文件路径（主键）
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dataset_name: 数据集名称
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cluster_id: 目标集群ID
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返回:
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dataset_target_id: 数据集绑定ID（已上传则返回现有ID，新上传则返回新ID）
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"""
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global dataset_map
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# 步骤1: 检查数据集是否已上传
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dataset = get_dataset_status(file_location, dataset_name, cluster_id)
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# 步骤2: 若未上传，则执行上传
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if not dataset or not dataset["is_uploaded"] or cluster_id not in dataset["upload_cluster"]:
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dataset = upload_dataset(file_location, dataset_name, cluster_id)
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if not dataset:
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return None # 上传失败
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return dataset["dataset_target_id"]
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def get_dataset_status(file_location: str, dataset_name: str, cluster_id: str) -> Optional[Dict]:
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"""
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检查数据集状态（是否已上传到指定集群）
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返回:
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数据集信息字典（若存在且已上传到指定集群），否则返回None
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"""
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with dataset_lock:
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if file_location in dataset_map:
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dataset = dataset_map[file_location]
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# 验证是否已上传到目标集群
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if dataset["is_uploaded"] and cluster_id in dataset["upload_cluster"]:
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logger.info(
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f"数据集 {dataset_name} 已上传到集群 {cluster_id} | target_id: {dataset['dataset_target_id']}")
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return dataset
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return None
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def upload_dataset(file_location: str, dataset_name: str, cluster_id: str) -> Optional[Dict]:
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"""
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执行数据集上传流程
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返回:
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上传成功后的数据集信息字典，失败则返回None
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"""
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dataset_path = os.path.join(file_location, dataset_name)
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# 检查本地文件是否存在
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if not os.path.exists(dataset_path):
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logger.error(f"数据集本地文件不存在 | path: {dataset_path}")
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return None
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# 计算文件大小（字节）
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try:
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file_size = os.path.getsize(dataset_path)
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except OSError as e:
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logger.error(f"获取文件大小失败 | path: {dataset_path} | 错误: {str(e)}")
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return None
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logger.info(f"开始上传数据集 {dataset_name} 到集群 {cluster_id} | path: {dataset_path}")
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try:
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# 获取认证Token
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token = get_token()
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if not token:
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logger.error("获取Token失败，无法上传数据集")
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return None
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headers = {'Authorization': f'Bearer {token}'}
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package_name = f"dataset-{dataset_name.split('.')[0]}-{uuid4().hex[:6]}" # 生成唯一文件夹名
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# 1. 创建数据集文件夹
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create_payload = {
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"userID": 5,
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"name": package_name,
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"dataType": "dataset",
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"packageID": 0,
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"uploadPriority": {"type": "specify", "clusters": [cluster_id]},
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"bindingInfo": {
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"clusterIDs": [cluster_id],
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"name": package_name,
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"category": "image",
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"type": "dataset",
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"imageID": "",
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"chip": ["ASCEND"],
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"packageID": 0
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}
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}
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create_resp = requests.post(
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API_CONFIG["create_package"]["url"],
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json=create_payload,
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headers=headers,
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timeout=API_CONFIG["create_package"]["timeout"]
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)
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create_resp.raise_for_status()
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create_result = create_resp.json()
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if create_result.get("code") != "OK":
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raise ValueError(f"创建文件夹失败 | 响应: {create_result}")
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packageID = create_result["data"]["newPackage"]["packageID"]
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logger.info(f"数据集文件夹创建成功 | packageID: {packageID}")
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# 2. 上传文件
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info_data = {
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"userID": 5,
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"packageID": packageID,
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"loadTo": [3],
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"loadToPath": [f"/dataset/5/{package_name}/"]
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}
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with open(dataset_path, 'rb') as f:
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form_data = {"info": (None, json.dumps(info_data)), "files": f}
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upload_resp = requests.post(
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API_CONFIG["upload_file"]["url"],
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files=form_data,
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headers=headers,
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timeout=API_CONFIG["upload_file"]["timeout"]
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)
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upload_resp.raise_for_status()
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upload_result = upload_resp.json()
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if upload_result.get("code") != "OK":
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raise ValueError(f"文件上传失败 | 响应: {upload_result}")
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object_id = upload_result["data"]["uploadeds"][0]["objectID"]
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logger.info(f"数据集文件上传成功 | objectID: {object_id}")
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# 3. 通知上传完成
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notify_payload = {
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"userID": 5,
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"packageID": packageID,
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"uploadParams": {
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"dataType": "dataset",
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"uploadInfo": {"type": "local", "localPath": dataset_name, "objectIDs": [object_id]}
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}
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}
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notify_resp = requests.post(
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API_CONFIG["notify_upload"]["url"],
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json=notify_payload,
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headers=headers,
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timeout=API_CONFIG["notify_upload"]["timeout"]
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)
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notify_resp.raise_for_status()
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# 4. 绑定到集群
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bind_payload = {
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"userID": 5,
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"info": {"type": "dataset", "packageID": packageID, "clusterIDs": [cluster_id]}
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}
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bind_resp = requests.post(
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API_CONFIG["bind_cluster"]["url"],
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json=bind_payload,
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headers=headers,
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timeout=API_CONFIG["bind_cluster"]["timeout"]
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)
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bind_resp.raise_for_status()
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# 5. 查询绑定ID（dataset_target_id）
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query_resp = requests.post(
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API_CONFIG["query_binding"]["url"],
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json={"dataType": "dataset", "param": {"userID": 5, "bindingID": -1, "type": "private"}},
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headers=headers,
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timeout=API_CONFIG["query_binding"]["timeout"]
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).json()
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if query_resp.get("code") != "OK":
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raise ValueError(f"查询绑定ID失败 | 响应: {query_resp}")
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dataset_target_id = None
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for item in query_resp["data"]["datas"]:
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if item["info"]["name"] == package_name:
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dataset_target_id = item["ID"]
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break
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if not dataset_target_id:
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raise ValueError(f"未找到数据集 {package_name} 的绑定ID")
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# 上传成功，创建并保存数据集信息
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dataset = DatasetInfo(
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file_location=file_location,
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name=dataset_name,
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size=file_size,
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is_uploaded=True,
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upload_cluster=[cluster_id],
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upload_time=time.strftime("%Y-%m-%d %H:%M:%S"),
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dataset_target_id=dataset_target_id
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)
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with dataset_lock:
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dataset_map[file_location] = dataset
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logger.info(f"数据集 {dataset_name} 上传成功 | target_id: {dataset_target_id}")
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return dataset
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except Exception as e:
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logger.error(f"数据集上传失败 | name: {dataset_name} | 错误: {str(e)}", exc_info=True)
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return None
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# 4. 绑定到集群
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bind_payload = {
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"userID": 5,
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"info": {"type": "dataset", "packageID": packageID, "clusterIDs": [cluster_id]}
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}
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bind_resp = requests.post(
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API_CONFIG["bind_cluster"]["url"],
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json=bind_payload,
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headers=headers,
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timeout=API_CONFIG["bind_cluster"]["timeout"]
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)
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bind_resp.raise_for_status()
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# 5. 查询绑定ID（dataset_target_id）
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query_resp = requests.post(
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API_CONFIG["query_binding"]["url"],
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json={"dataType": "dataset", "param": {"userID": 5, "bindingID": -1, "type": "private"}},
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headers=headers,
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||
timeout=API_CONFIG["query_binding"]["timeout"]
|
||
).json()
|
||
if query_resp.get("code") != "OK":
|
||
raise ValueError(f"查询绑定ID失败 | 响应: {query_resp}")
|
||
|
||
dataset_target_id = None
|
||
for item in query_resp["data"]["datas"]:
|
||
if item["info"]["name"] == package_name:
|
||
dataset_target_id = item["ID"]
|
||
break
|
||
if not dataset_target_id:
|
||
raise ValueError(f"未找到数据集 {package_name} 的绑定ID")
|
||
|
||
# 上传成功，更新全局映射
|
||
with dataset_lock:
|
||
dataset = DatasetInfo(
|
||
file_location=file_location,
|
||
name=dataset_name,
|
||
size=file_size,
|
||
is_uploaded=True,
|
||
upload_cluster=[cluster_id],
|
||
upload_time=time.strftime("%Y-%m-%d %H:%M:%S"),
|
||
dataset_target_id=dataset_target_id
|
||
)
|
||
dataset_map[file_location] = dataset
|
||
logger.info(f"数据集 {dataset_name} 上传成功 | target_id: {dataset_target_id}")
|
||
|
||
return dataset_target_id
|
||
|
||
except Exception as e:
|
||
logger.error(f"数据集上传失败 | name: {dataset_name} | 错误: {str(e)}", exc_info=True)
|
||
return None
|
||
|
||
|
||
# -------------------------- API调用方法 --------------------------
|
||
|
||
def get_token() -> Optional[str]:
|
||
"""获取认证Token"""
|
||
login_payload = {"username": "admin", "password": "Nudt@123"}
|
||
try:
|
||
config = API_CONFIG["login"]
|
||
response = requests.post(config["url"], json=login_payload, timeout=config["timeout"])
|
||
response.raise_for_status()
|
||
result = response.json()
|
||
if result.get("code") == 200 and "data" in result and "token" in result["data"]:
|
||
logger.info("Token获取成功")
|
||
return result["data"]["token"]
|
||
else:
|
||
logger.error(f"Token获取失败 | 响应: {result}")
|
||
return None
|
||
except requests.exceptions.RequestException as e:
|
||
logger.error(f"登录请求异常: {str(e)}", exc_info=True)
|
||
return None
|
||
|
||
|
||
def submit_single_task(task: Dict) -> bool:
|
||
"""提交单个任务到集群（使用模板中的son_code_id）"""
|
||
token = get_token()
|
||
if not token:
|
||
with task_map_lock:
|
||
task["status"] = TASK_STATUS["FAILED"]
|
||
task["error_msg"] = "获取Token失败"
|
||
return False
|
||
|
||
# 获取选择的集群ID
|
||
cluster_id = task.get("cluster_id")
|
||
if not cluster_id:
|
||
with task_map_lock:
|
||
task["status"] = TASK_STATUS["FAILED"]
|
||
task["error_msg"] = "未指定集群ID"
|
||
logger.error(f"[{task['task_name']}] 提交失败：未指定集群ID")
|
||
return False
|
||
|
||
# -------------------------- 新增：数据集检查与上传 --------------------------
|
||
# 在创建文件夹前先检查数据集状态
|
||
dataset_target_id = check_and_handle_dataset(
|
||
file_location=task["file_location"],
|
||
dataset_name=task["dataset_name"],
|
||
cluster_id=cluster_id
|
||
)
|
||
if not dataset_target_id:
|
||
# 数据集上传失败，释放集群资源
|
||
with cluster_lock:
|
||
if cluster_id in cluster_resources:
|
||
for res_type, required in task["resource"].items():
|
||
if res_type in cluster_resources[cluster_id]["available"]:
|
||
cluster_resources[cluster_id]["available"][res_type] += required
|
||
with task_map_lock:
|
||
task["status"] = TASK_STATUS["FAILED"]
|
||
task["error_msg"] = "数据集上传失败"
|
||
logger.error(f"[{task['task_name']}] 提交失败：数据集处理失败")
|
||
return False
|
||
# --------------------------------------------------------------------------
|
||
|
||
headers = {
|
||
'Content-Type': 'application/json',
|
||
'Authorization': f'Bearer {token}'
|
||
}
|
||
task_name = task["task_name"]
|
||
package_name = task["package_name"]
|
||
son_code_id = task["son_code_id"] # 直接使用任务中的子算法ID（来自模板）
|
||
|
||
try:
|
||
# 第一步：创建数据集文件夹（使用选中的集群ID）- 复用原有逻辑但无需重复上传
|
||
config = API_CONFIG["create_package"]
|
||
create_payload = {
|
||
"userID": 5,
|
||
"name": package_name,
|
||
"dataType": "dataset",
|
||
"packageID": 0,
|
||
"uploadPriority": {"type": "specify", "clusters": [cluster_id]},
|
||
"bindingInfo": {
|
||
"clusterIDs": [cluster_id],
|
||
"name": package_name,
|
||
"category": "image",
|
||
"type": "dataset",
|
||
"imageID": "",
|
||
"bias": [],
|
||
"region": [],
|
||
"chip": ["ASCEND"],
|
||
"selectedCluster": [],
|
||
"modelType": "",
|
||
"env": "",
|
||
"version": "",
|
||
"packageID": 0,
|
||
"points": 0
|
||
}
|
||
}
|
||
create_resp = requests.post(config["url"], json=create_payload, headers=headers, timeout=config["timeout"])
|
||
create_resp.raise_for_status()
|
||
create_result = create_resp.json()
|
||
if create_result.get("code") != "OK":
|
||
raise ValueError(f"创建文件夹失败 | API返回: {create_result}")
|
||
packageID = create_result["data"]["newPackage"]["packageID"]
|
||
logger.info(f"[{task_name}] 第一步：创建文件夹成功 | packageID: {packageID} | 集群: {cluster_id}")
|
||
|
||
# 第二步：通知上传完成（复用已上传的数据集）
|
||
config = API_CONFIG["notify_upload"]
|
||
notify_payload = {
|
||
"userID": 5,
|
||
"packageID": packageID,
|
||
"uploadParams": {
|
||
"dataType": "dataset",
|
||
"uploadInfo": {"type": "local", "localPath": task["dataset_name"], "objectIDs": []}
|
||
}
|
||
}
|
||
notify_resp = requests.post(config["url"], json=notify_payload, headers=headers, timeout=config["timeout"])
|
||
notify_resp.raise_for_status()
|
||
logger.info(f"[{task_name}] 第二步：通知上传完成成功")
|
||
|
||
# 第三步：绑定数据集到集群（使用选中的集群ID）
|
||
config = API_CONFIG["bind_cluster"]
|
||
bind_payload = {
|
||
"userID": 5,
|
||
"info": {"type": "dataset", "packageID": packageID, "clusterIDs": [cluster_id]}
|
||
}
|
||
bind_resp = requests.post(config["url"], json=bind_payload, headers=headers, timeout=config["timeout"])
|
||
bind_resp.raise_for_status()
|
||
logger.info(f"[{task_name}] 第三步：数据集绑定集群 {cluster_id} 成功")
|
||
|
||
# 第四步：提交训练任务（使用已获取的dataset_target_id）
|
||
config = API_CONFIG["submit_task"]
|
||
task_res = task["resource"]
|
||
|
||
submit_payload = {
|
||
"userID": 5,
|
||
"jobSetInfo": {
|
||
"jobs": [
|
||
{
|
||
"localJobID": "1",
|
||
"name": task_name,
|
||
"description": "自动提交的训练任务",
|
||
"type": "AI",
|
||
"files": {
|
||
"dataset": {"type": "Binding", "bindingID": dataset_target_id}, # 使用检查阶段获取的ID
|
||
"model": {"type": "Binding", "bindingID": ""},
|
||
"image": {"type": "Image", "imageID": 11}
|
||
},
|
||
"jobResources": {
|
||
"scheduleStrategy": "dataLocality",
|
||
"clusters": [
|
||
{
|
||
"clusterID": cluster_id,
|
||
"runtime": {"envs": {}, "params": {}},
|
||
"code": {"type": "Binding", "bindingID": son_code_id},
|
||
"resources": [
|
||
{"type": "CPU", "name": "ARM", "number": task_res["CPU"]},
|
||
{"type": "MEMORY", "name": "RAM", "number": task_res["MEMORY"]},
|
||
{"type": "MEMORY", "name": "VRAM", "number": 32},
|
||
{"type": "STORAGE", "name": "DISK", "number": 886},
|
||
{"type": "NPU", "name": "ASCEND910", "number": task_res.get("NPU", 0)}
|
||
]
|
||
}
|
||
]
|
||
}
|
||
}
|
||
]
|
||
}
|
||
}
|
||
|
||
response = requests.post(
|
||
config["url"],
|
||
json=submit_payload,
|
||
headers=headers,
|
||
timeout=config["timeout"]
|
||
)
|
||
response.raise_for_status()
|
||
submit_resp = response.json()
|
||
if submit_resp.get("code") != "OK":
|
||
raise ValueError(f"任务提交失败 | API返回: {submit_resp}")
|
||
|
||
third_party_task_id = submit_resp.get('data', {}).get('jobSetID')
|
||
logger.info(f"[{task_name}] 第四步：任务提交至集群 {cluster_id} 成功 | 云际任务ID: {third_party_task_id}")
|
||
|
||
# 更新任务状态为成功
|
||
with task_map_lock:
|
||
task["status"] = TASK_STATUS["SUCCEED"]
|
||
task["third_party_task_id"] = third_party_task_id
|
||
return True
|
||
|
||
except Exception as e:
|
||
error_msg = f"提交失败: {str(e)}"
|
||
# 任务失败时释放集群资源
|
||
with cluster_lock:
|
||
if cluster_id in cluster_resources:
|
||
for res_type, required in task["resource"].items():
|
||
if res_type in cluster_resources[cluster_id]["available"]:
|
||
cluster_resources[cluster_id]["available"][res_type] += required
|
||
logger.info(f"任务失败，释放集群 {cluster_id} 资源: {task['resource']}")
|
||
|
||
with task_map_lock:
|
||
task["fail_count"] += 1
|
||
if task["fail_count"] >= task["max_fail_threshold"]:
|
||
task["status"] = TASK_STATUS["RETRY_EXHAUSTED"]
|
||
else:
|
||
task["status"] = TASK_STATUS["FAILED"]
|
||
task["error_msg"] = error_msg
|
||
logger.error(f"[{task_name}] {error_msg}", exc_info=True)
|
||
return False
|
||
|
||
|
||
def query_third_party_task_status(third_party_task_id: str) -> Optional[Dict]:
|
||
"""
|
||
查询云际平台任务状态（适配新的list接口）
|
||
参数:
|
||
third_party_task_id: 云际任务ID
|
||
返回:
|
||
包含状态和时间的字典（如{"status": "Succeeded", "end_time": "2025-07-17T20:57:30+08:00"}）
|
||
若未找到任务或查询失败，返回None
|
||
"""
|
||
if not third_party_task_id:
|
||
logger.warning("云际任务ID为空，无法查询状态")
|
||
return None
|
||
try:
|
||
# 配置新的API地址和参数
|
||
url = "http://119.45.255.234:30180/pcm/v1/core/task/list"
|
||
params = {
|
||
"pageNum": 1,
|
||
"pageSize": 10,
|
||
"type": 1
|
||
}
|
||
# 获取认证Token
|
||
token = get_token()
|
||
if not token:
|
||
logger.error("获取Token失败，无法查询任务状态")
|
||
return None
|
||
headers = {"Authorization": f"Bearer {token}"} if token else {}
|
||
# 发送GET请求
|
||
response = requests.get(
|
||
url,
|
||
params=params,
|
||
headers=headers,
|
||
timeout=15 # 超时设置
|
||
)
|
||
response.raise_for_status() # 校验HTTP状态码
|
||
result = response.json()
|
||
# 校验接口返回状态
|
||
if result.get("code") != 200 or "data" not in result or "list" not in result["data"]:
|
||
logger.error(f"查询任务状态接口返回异常 | 响应: {result}")
|
||
return None
|
||
# 遍历任务列表查找目标任务
|
||
task_list = result["data"]["list"]
|
||
target_task = None
|
||
for task in task_list:
|
||
if task.get("id") == third_party_task_id:
|
||
target_task = task
|
||
break
|
||
if not target_task:
|
||
logger.warning(f"在任务列表中未找到ID为 {third_party_task_id} 的任务")
|
||
return None
|
||
# 提取状态和时间信息
|
||
task_status = target_task.get("status")
|
||
end_time = target_task.get("endTime") # 成功/失败时间
|
||
# 日志记录查询结果
|
||
logger.info(
|
||
f"任务 {third_party_task_id} 查询成功 | 状态: {task_status} | "
|
||
f"结束时间: {end_time or '未结束'}"
|
||
)
|
||
return {
|
||
"status": task_status,
|
||
"end_time": end_time
|
||
}
|
||
except requests.exceptions.RequestException as e:
|
||
logger.error(
|
||
f"查询任务状态请求异常 | 任务ID: {third_party_task_id} | 错误: {str(e)}",
|
||
exc_info=True
|
||
)
|
||
return None
|
||
except Exception as e:
|
||
logger.error(
|
||
f"解析任务状态失败 | 任务ID: {third_party_task_id} | 错误: {str(e)}",
|
||
exc_info=True
|
||
)
|
||
return None
|
||
|
||
|
||
# -------------------------- 线程一：任务监控线程 --------------------------
|
||
class TaskMonitorThread(threading.Thread):
|
||
"""监控线程：专注监控任务状态"""
|
||
|
||
def __init__(self, check_interval: int = 10):
|
||
super().__init__(name="TaskMonitorThread")
|
||
self.check_interval = check_interval
|
||
self._stop_event = threading.Event()
|
||
|
||
def run(self) -> None:
|
||
logger.info(f"监控线程启动 | 监控间隔: {self.check_interval}秒")
|
||
while not self._stop_event.is_set():
|
||
with task_map_lock:
|
||
tasks = list(task_map.values())
|
||
|
||
for task in tasks:
|
||
with task_map_lock:
|
||
current_status = task["status"]
|
||
|
||
if current_status == TASK_STATUS["SUBMITTED"]:
|
||
continue
|
||
|
||
elif current_status == TASK_STATUS["SUBMITTING"]:
|
||
if not task["third_party_task_id"]:
|
||
logger.warning(f"任务 {task['task_name']} 无云际ID，跳过状态查询")
|
||
continue
|
||
|
||
# 在TaskMonitorThread的run方法中替换原有调用逻辑
|
||
third_party_info = query_third_party_task_status(task["third_party_task_id"])
|
||
if third_party_info:
|
||
with task_map_lock:
|
||
if third_party_info["status"] == "Succeeded":
|
||
task["status"] = TASK_STATUS["SUCCEED"]
|
||
task["success_time"] = third_party_info["end_time"] # 记录成功时间
|
||
logger.info(
|
||
f"任务状态更新 | task_name: {task['task_name']} | 提交成功 | "
|
||
f"成功时间: {task['success_time']}"
|
||
)
|
||
elif third_party_info["status"] == "Failed" or "Saved":
|
||
task["status"] = TASK_STATUS["FAILED"]
|
||
task["fail_count"] += 1 # 失败次数加一
|
||
task["error_msg"] = f"云际任务执行失败（第{task['fail_count']}次）"
|
||
logger.warning(
|
||
f"任务状态更新 | task_name: {task['task_name']} | 提交失败 | "
|
||
f"失败次数: {task['fail_count']}/{task['max_fail_threshold']}"
|
||
)
|
||
|
||
if self._check_all_tasks_completed():
|
||
logger.info("所有任务已完成")
|
||
self.stop()
|
||
|
||
self._stop_event.wait(self.check_interval)
|
||
logger.info("监控线程结束")
|
||
|
||
def _check_all_tasks_completed(self) -> bool:
|
||
"""检查是否所有任务已完成"""
|
||
with task_map_lock:
|
||
for task in task_map.values():
|
||
if task["status"] in [TASK_STATUS["SUBMITTED"], TASK_STATUS["SUBMITTING"]]:
|
||
return False
|
||
if task["status"] == TASK_STATUS["FAILED"] and task["fail_count"] < task["max_fail_threshold"]:
|
||
return False
|
||
return True
|
||
|
||
def stop(self) -> None:
|
||
self._stop_event.set()
|
||
|
||
|
||
# -------------------------- 线程二：任务提交线程 --------------------------
|
||
class TaskSubmitThread(threading.Thread):
|
||
"""提交线程：按状态判断是否提交"""
|
||
|
||
def __init__(self, max_workers: int = 3):
|
||
super().__init__(name="TaskSubmitThread")
|
||
self.max_workers = max_workers
|
||
self._stop_event = threading.Event()
|
||
|
||
def run(self) -> None:
|
||
logger.info(f"提交线程启动 | 并发数: {self.max_workers}")
|
||
while not self._stop_event.is_set():
|
||
# 筛选待提交任务
|
||
with task_map_lock:
|
||
pending_tasks = []
|
||
for task in task_map.values():
|
||
status = task["status"]
|
||
if status == TASK_STATUS["SUBMITTED"]:
|
||
pending_tasks.append(task)
|
||
elif status == TASK_STATUS["FAILED"] and task["fail_count"] < task["max_fail_threshold"]:
|
||
pending_tasks.append(task)
|
||
elif status == TASK_STATUS["FAILED"]:
|
||
logger.info(
|
||
f"任务 {task['task_name']} 失败次数超阈值，停止提交")
|
||
|
||
# 并发提交任务
|
||
with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as executor:
|
||
futures = {executor.submit(self.commit_task, task): task for task in pending_tasks}
|
||
for future in concurrent.futures.as_completed(futures):
|
||
task = futures[future]
|
||
try:
|
||
future.result()
|
||
except Exception as e:
|
||
with task_map_lock:
|
||
task["status"] = TASK_STATUS["FAILED"]
|
||
task["fail_count"] += 1
|
||
task["error_msg"] = f"提交过程异常: {str(e)}"
|
||
logger.error(f"任务提交异常 | task_name: {task['task_name']} | 错误: {str(e)}")
|
||
|
||
if self._check_all_tasks_completed():
|
||
logger.info("所有任务已完成，提交线程退出")
|
||
self.stop()
|
||
break
|
||
|
||
if not pending_tasks:
|
||
logger.info("无待提交任务，等待下次检查")
|
||
self._stop_event.wait(5)
|
||
|
||
logger.info("提交线程结束")
|
||
|
||
def commit_task(self, task: Dict) -> None:
|
||
"""提交任务入口：先选集群，再提交"""
|
||
cluster_id = select_cluster(task["resource"])
|
||
if not cluster_id:
|
||
with task_map_lock:
|
||
task["status"] = TASK_STATUS["FAILED"]
|
||
task["fail_count"] += 1
|
||
task["error_msg"] = "无可用集群"
|
||
logger.error(f"[{task['task_name']}] 提交失败：无可用集群")
|
||
return
|
||
|
||
# 标记为提交中
|
||
with task_map_lock:
|
||
task["status"] = TASK_STATUS["SUBMITTING"]
|
||
task["cluster_id"] = cluster_id
|
||
logger.info(f"[{task['task_name']}] 开始提交至集群 {cluster_id}")
|
||
|
||
# 执行提交
|
||
submit_success = submit_single_task(task)
|
||
if not submit_success:
|
||
logger.warning(f"[{task['task_name']}] 提交失败，等待重试（当前失败次数：{task['fail_count']}）")
|
||
|
||
def _check_all_tasks_completed(self) -> bool:
|
||
"""检查是否所有任务已完成"""
|
||
with task_map_lock:
|
||
for task in task_map.values():
|
||
if task["status"] in [TASK_STATUS["SUBMITTED"], TASK_STATUS["SUBMITTING"]]:
|
||
return False
|
||
if task["status"] == TASK_STATUS["FAILED"] and task["fail_count"] < task["max_fail_threshold"]:
|
||
return False
|
||
return True
|
||
|
||
def stop(self) -> None:
|
||
self._stop_event.set()
|
||
|
||
|
||
# -------------------------- 主程序 --------------------------
|
||
if __name__ == "__main__":
|
||
task_templates = generate_task_templates()
|
||
load_tasks_to_queue(task_templates)
|
||
|
||
monitor_thread = TaskMonitorThread(check_interval=10)
|
||
monitor_thread.start()
|
||
|
||
submit_thread = TaskSubmitThread(max_workers=1)
|
||
submit_thread.start()
|
||
|
||
monitor_thread.join()
|
||
submit_thread.join()
|
||
logger.info("所有任务处理完毕，程序退出") |